Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp; Alexandre Tkatchenko; Klaus-Robert Müller; O. Anatole von Lilienfeld

doi:10.1103/physrevlett.108.058301

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arXiv · 1109.2618

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp·Alexandre Tkatchenko·Klaus-Robert Müller·O. Anatole von Lilienfeld

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Abstract

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.

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Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld. 2011-09-12. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. https://doi.org/10.1103/physrevlett.108.058301

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